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        "%matplotlib inline"
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        "\n# Plotting Learning Curves\n\n\nOn the left side the learning curve of a naive Bayes classifier is shown for\nthe digits dataset. Note that the training score and the cross-validation score\nare both not very good at the end. However, the shape of the curve can be found\nin more complex datasets very often: the training score is very high at the\nbeginning and decreases and the cross-validation score is very low at the\nbeginning and increases. On the right side we see the learning curve of an SVM\nwith RBF kernel. We can see clearly that the training score is still around\nthe maximum and the validation score could be increased with more training\nsamples.\n\n"
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      "source": [
        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.svm import SVC\nfrom sklearn.datasets import load_digits\nfrom sklearn.model_selection import learning_curve\nfrom sklearn.model_selection import ShuffleSplit\n\n\ndef plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n                        n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\n    \"\"\"\n    Generate a simple plot of the test and training learning curve.\n\n    Parameters\n    ----------\n    estimator : object type that implements the \"fit\" and \"predict\" methods\n        An object of that type which is cloned for each validation.\n\n    title : string\n        Title for the chart.\n\n    X : array-like, shape (n_samples, n_features)\n        Training vector, where n_samples is the number of samples and\n        n_features is the number of features.\n\n    y : array-like, shape (n_samples) or (n_samples, n_features), optional\n        Target relative to X for classification or regression;\n        None for unsupervised learning.\n\n    ylim : tuple, shape (ymin, ymax), optional\n        Defines minimum and maximum yvalues plotted.\n\n    cv : int, cross-validation generator or an iterable, optional\n        Determines the cross-validation splitting strategy.\n        Possible inputs for cv are:\n          - None, to use the default 3-fold cross-validation,\n          - integer, to specify the number of folds.\n          - :term:`CV splitter`,\n          - An iterable yielding (train, test) splits as arrays of indices.\n\n        For integer/None inputs, if ``y`` is binary or multiclass,\n        :class:`StratifiedKFold` used. If the estimator is not a classifier\n        or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.\n\n        Refer :ref:`User Guide <cross_validation>` for the various\n        cross-validators that can be used here.\n\n    n_jobs : int or None, optional (default=None)\n        Number of jobs to run in parallel.\n        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n        for more details.\n\n    train_sizes : array-like, shape (n_ticks,), dtype float or int\n        Relative or absolute numbers of training examples that will be used to\n        generate the learning curve. If the dtype is float, it is regarded as a\n        fraction of the maximum size of the training set (that is determined\n        by the selected validation method), i.e. it has to be within (0, 1].\n        Otherwise it is interpreted as absolute sizes of the training sets.\n        Note that for classification the number of samples usually have to\n        be big enough to contain at least one sample from each class.\n        (default: np.linspace(0.1, 1.0, 5))\n    \"\"\"\n    plt.figure()\n    plt.title(title)\n    if ylim is not None:\n        plt.ylim(*ylim)\n    plt.xlabel(\"Training examples\")\n    plt.ylabel(\"Score\")\n    train_sizes, train_scores, test_scores = learning_curve(\n        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n    train_scores_mean = np.mean(train_scores, axis=1)\n    train_scores_std = np.std(train_scores, axis=1)\n    test_scores_mean = np.mean(test_scores, axis=1)\n    test_scores_std = np.std(test_scores, axis=1)\n    plt.grid()\n\n    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n                     train_scores_mean + train_scores_std, alpha=0.1,\n                     color=\"r\")\n    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n             label=\"Training score\")\n    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n             label=\"Cross-validation score\")\n\n    plt.legend(loc=\"best\")\n    return plt\n\n\ndigits = load_digits()\nX, y = digits.data, digits.target\n\n\ntitle = \"Learning Curves (Naive Bayes)\"\n# Cross validation with 100 iterations to get smoother mean test and train\n# score curves, each time with 20% data randomly selected as a validation set.\ncv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)\n\nestimator = GaussianNB()\nplot_learning_curve(estimator, title, X, y, ylim=(0.7, 1.01), cv=cv, n_jobs=4)\n\ntitle = \"Learning Curves (SVM, RBF kernel, $\\gamma=0.001$)\"\n# SVC is more expensive so we do a lower number of CV iterations:\ncv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)\nestimator = SVC(gamma=0.001)\nplot_learning_curve(estimator, title, X, y, (0.7, 1.01), cv=cv, n_jobs=4)\n\nplt.show()"
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